Development, Application, and Evaluation of Prediction Models for Cancer Risk and Prognosis issued by the National Cancer Institute (NCI) supports research on innovative statistical methods for developing and evaluating new and existing cancer risk prediction models. Predictive accuracy is a key factor for determining clinical applicability of cancer risk prediction models in identifying high-risk individuals for ealy prevention. With continuously emerging risk factors for cancer, there is a pressing need for timely assessment of their added values for prediction. But study designs that balance cost and statistical efficiency and accompanying statistical methods are mostly lacking. This proposal responds to this PA by developing and evaluating cost-effective two-phase stratified case-control study designs and statistical inference methods for developing and evaluating absolute risk prediction models for cancer. Our proposed work is built on a popular method that integrates case-control data and external hazard rates of cancer and mortality to predict cancer absolute risk. As a showcase example for our statistical methods and accompanying software, we explore the added value of volumetric breast density for predicting breast cancer risk.